{ "decode": { "code": { "completion_gate": { "passed": 5, "total": 5 }, "decode_tokens_per_second": { "maximum": 44.894, "median": 43.962, "minimum": 43.681, "p90": 44.894 }, "pooled_decode_tokens_per_second": 44.15, "runs": [ { "cached_prompt_tokens": 0, "completion_tokens": 2486, "completion_validation": { "valid": true }, "decode_seconds": 55.353083, "decode_tokens_per_second": 44.894, "finish_reason": "stop", "measured_sse_events": 847, "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock is an injectable source of monotonic wall time.\ntype Clock interface {\n\tNow() time.Time\n}\n\n// RealClock returns the current wall time.\ntype RealClock struct{}\n\nfunc (RealClock) Now() time.Time { return time.Now() }\n\n// ErrInvalidConfig is returned when limiter parameters are nonsensical.\nvar ErrInvalidConfig = errors.New(\"ratelimit: invalid configuration\")\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\ntype Limiter struct {\n\tmu sync.Mutex\n\tclock Clock\n\tcapacity float64\n\trate float64 // tokens per second\n\ttokens float64\n\tlast time.Time\n\tstartTime time.Time\n}\n\n// NewLimiter creates a limiter with the given bucket capacity and refill rate\n// (tokens per second). The bucket starts full. A nil clock uses RealClock.\nfunc NewLimiter(capacity, rate float64, clock Clock) (*Limiter, error) {\n\tif capacity <= 0 || rate <= 0 {\n\t\treturn nil, ErrInvalidConfig\n\t}\n\tif clock == nil {\n\t\tclock = RealClock{}\n\t}\n\tnow := clock.Now()\n\treturn &Limiter{\n\t\tclock: clock,\n\t\tcapacity: capacity,\n\t\trate: rate,\n\t\ttokens: capacity,\n\t\tlast: now,\n\t\tstartTime: now,\n\t}, nil\n}\n\n// AllowN reports whether n tokens are available, consuming them if so.\n// It returns false for n <= 0 or n > capacity without consuming tokens.\nfunc (l *Limiter) AllowN(n int) bool {\n\tif n <= 0 {\n\t\treturn false\n\t}\n\tl.mu.Lock()\n\tdefer l.mu.Unlock()\n\tif float64(n) > l.capacity {\n\t\treturn false\n\t}\n\tl.refill()\n\tif l.tokens < float64(n) {\n\t\treturn false\n\t}\n\tl.tokens -= float64(n)\n\treturn true\n}\n\n// refill accrues tokens elapsed since the last observation, capped at capacity.\nfunc (l *Limiter) refill() {\n\tnow := l.clock.Now()\n\tif !now.After(l.last) {\n\t\treturn\n\t}\n\telapsed := now.Sub(l.last).Seconds()\n\tl.last = now\n\tl.tokens += elapsed * l.rate\n\tif l.tokens > l.capacity {\n\t\tl.tokens = l.capacity\n\t}\n}\n\n// Available returns the current token count, rounded down. Useful for tests.\nfunc (l *Limiter) Available() int {\n\tl.mu.Lock()\n\tdefer l.mu.Unlock()\n\tl.refill()\n\tif l.tokens < 0 {\n\t\treturn 0\n\t}\n\treturn int(l.tokens)\n}\n```\n\n```go\n// ratelimit_test.go\npackage ratelimit\n\nimport (\n\t\"sync\"\n\t\"testing\"\n\t\"time\"\n)\n\n// fakeClock is a controllable Clock for deterministic tests.\ntype fakeClock struct {\n\tmu sync.Mutex\n\tnow time.Time\n}\n\nfunc newFakeClock(t time.Time) *fakeClock { return &fakeClock{now: t} }\n\nfunc (c *fakeClock) Now() time.Time {\n\tc.mu.Lock()\n\tdefer c.mu.Unlock()\n\treturn c.now\n}\n\nfunc (c *fakeClock) Advance(d time.Duration) {\n\tc.mu.Lock()\n\tdefer c.mu.Unlock()\n\tc.now = c.now.Add(d)\n}\n\nfunc TestNewLimiterValidation(t *testing.T) {\n\tcases := []struct {\n\t\tname string\n\t\tcapacity float64\n\t\trate float64\n\t\twantErr error\n\t}{\n\t\t{\"zero capacity\", 0, 1, ErrInvalidConfig},\n\t\t{\"negative capacity\", -1, 1, ErrInvalidConfig},\n\t\t{\"zero rate\", 1, 0, ErrInvalidConfig},\n\t\t{\"negative rate\", 1, -1, ErrInvalidConfig},\n\t}\n\tfor _, tc := range cases {\n\t\tt.Run(tc.name, func(t *testing.T) {\n\t\t\t_, err := NewLimiter(tc.capacity, tc.rate, newFakeClock(time.Unix(0, 0)))\n\t\t\tif err != tc.wantErr {\n\t\t\t\tt.Fatalf(\"NewLimiter(%v, %v) err = %v, want %v\", tc.capacity, tc.rate, err, tc.wantErr)\n\t\t\t}\n\t\t})\n\t}\n}\n\nfunc TestLimiterAllowN(t *testing.T) {\n\tbase := time.Unix(1000, 0)\n\tcases := []struct {\n\t\tname string\n\t\tcapacity float64\n\t\trate float64 // tokens per second\n\t\tsteps []struct {\n\t\t\tadvance time.Duration\n\t\t\tn int\n\t\t\twant bool\n\t\t}\n\t}{\n\t\t{\n\t\t\tname: \"starts full and drains\",\n\t\t\tcapacity: 3,\n\t\t\trate: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 1, true},\n\t\t\t\t{0, 1, true},\n\t\t\t\t{0, 1, true},\n\t\t\t\t{0, 1, false}, // empty, no time passed\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"refills over time\",\n\t\t\tcapacity: 2,\n\t\t\trate: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 2, true}, // drain full bucket\n\t\t\t\t{500 * time.Millisecond, 1, false}, // only 0.5 token accrued\n\t\t\t\t{500 * time.Millisecond, 1, true}, // now 1.0 token accrued\n\t\t\t\t{0, 1, false},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"caps at capacity\",\n\t\t\tcapacity: 1,\n\t\t\trate: 10,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 1, true},\n\t\t\t\t{10 * time.Second, 1, true}, // would accrue 100, capped at 1\n\t\t\t\t{0, 1, false},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"rejects n larger than capacity\",\n\t\t\tcapacity: 2,\n\t\t\trate: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 3, false},\n\t\t\t\t{0, 1, true},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"rejects non-positive n\",\n\t\t\tcapacity: 2,\n\t\t\trate: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 0, false},\n\t\t\t\t{0, -1, false},\n\t\t\t\t{0, 1, true},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"no advance on backward or zero clock movement\",\n\t\t\tcapacity: 1,\n\t\t\trate: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 1, true},\n\t\t\t\t{0, 1, false},\n\t\t\t},\n\t\t},\n\t}\n\tfor _, tc := range cases {\n\t\tt.Run(tc.name, func(t *testing.T) {\n\t\t\tfc := newFakeClock(base)\n\t\t\tl, err := NewLimiter(tc.capacity, tc.rate, fc)\n\t\t\tif err != nil {\n\t\t\t\tt.Fatalf(\"NewLimiter: %v\", err)\n\t\t\t}\n\t\t\tfor i, s := range tc.steps {\n\t\t\t\tfc.Advance(s.advance)\n\t\t\t\tif got := l.AllowN(s.n); got != s.want {\n\t\t\t\t\tt.Fatalf(\"step %d: AllowN(%d) = %v, want %v\", i, s.n, got, s.want)\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\t}\n}\n\nfunc TestLimiterConcurrent(t *testing.T) {\n\tfc := newFakeClock(time.Unix(0, 0))\n\tl, err := NewLimiter(1000, 1000, fc)\n\tif err != nil {\n\t\tt.Fatalf(\"NewLimiter: %v\", err)\n\t}\n\tvar wg sync.WaitGroup\n\tallowed := make(chan bool, 100)\n\tfor i := 0; i < 100; i++ {\n\t\twg.Add(1)\n\t\tgo func() {\n\t\t\tdefer wg.Done()\n\t\t\tallowed <- l.AllowN(1)\n\t\t}()\n\t}\n\twg.Wait()\n\tclose(allowed)\n\tcount := 0\n\tfor a := range allowed {\n\t\tif a {\n\t\t\tcount++\n\t\t}\n\t}\n\tif count != 100 {\n\t\tt.Fatalf(\"expected all 100 goroutines allowed from full bucket, got %d\", count)\n\t}\n\tif got := l.Available(); got != 900 {\n\t\tt.Fatalf(\"Available() = %d, want 900\", got)\n\t}\n}\n\nfunc TestLimiterNilClockUsesRealClock(t *testing.T) {\n\tl, err := NewLimiter(1, 1, nil)\n\tif err != nil {\n\t\tt.Fatalf(\"NewLimiter: %v\", err)\n\t}\n\tif !l.AllowN(1) {\n\t\tt.Fatal(\"expected initial AllowN(1) to succeed\")\n\t}\n\tif l.AllowN(1) {\n\t\tt.Fatal(\"expected second immediate AllowN(1) to fail\")\n\t}\n}\n```", "output_characters": 6364, "output_sha256": "453a4b7852e07d7823770e5f148c093b50eb569cf43862ab864d191aff71382f", "prompt_tokens": 148, "reasoning_characters": 0, "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "response_tail_seconds": 0.074368, "run": 1, "stream_done_marker": true, "stream_sha256": "453a4b7852e07d7823770e5f148c093b50eb569cf43862ab864d191aff71382f", "time_to_first_visible_seconds": 0.710262, "time_to_last_output_seconds": 56.063345, "ttft_seconds": 0.710262, "wall_seconds": 56.137713 }, { "cached_prompt_tokens": 0, "completion_tokens": 2251, "completion_validation": { "valid": true }, "decode_seconds": 51.341078, "decode_tokens_per_second": 43.825, "finish_reason": "stop", "measured_sse_events": 784, "output": "```go\n// ratelimit/ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time so tests can drive deterministic scenarios.\ntype Clock interface {\n\tNow() time.Time\n}\n\n// RealClock reports wall-clock time.\ntype RealClock struct{}\n\nfunc (RealClock) Now() time.Time { return time.Now() }\n\n// ErrInvalidConfig is returned by New when arguments are nonsensical.\nvar ErrInvalidConfig = errors.New(\"ratelimit: invalid configuration\")\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\ntype Limiter struct {\n\tmu sync.Mutex\n\tclock Clock\n\tcapacity float64\n\trate float64 // tokens per second\n\ttokens float64\n\tlast time.Time\n\tstartTime time.Time\n}\n\n// New builds a Limiter with the given bucket capacity and refill rate.\n// rate is tokens per second. A zero clock selects RealClock.\nfunc New(capacity, rate float64, clock Clock) (*Limiter, error) {\n\tif capacity <= 0 || rate <= 0 {\n\t\treturn nil, ErrInvalidConfig\n\t}\n\tif clock == nil {\n\t\tclock = RealClock{}\n\t}\n\tnow := clock.Now()\n\treturn &Limiter{\n\t\tclock: clock,\n\t\tcapacity: capacity,\n\t\trate: rate,\n\t\ttokens: capacity, // start full\n\t\tlast: now,\n\t\tstartTime: now,\n\t}, nil\n}\n\n// AllowN reports whether n tokens are available, consuming them if so.\n// n <= 0 is always allowed and consumes nothing.\nfunc (l *Limiter) AllowN(n int) bool {\n\tif n <= 0 {\n\t\treturn true\n\t}\n\tl.mu.Lock()\n\tdefer l.mu.Unlock()\n\tl.refill()\n\tif l.tokens < float64(n) {\n\t\treturn false\n\t}\n\tl.tokens -= float64(n)\n\treturn true\n}\n\n// Available returns the current token count, rounded down.\nfunc (l *Limiter) Available() int {\n\tl.mu.Lock()\n\tdefer l.mu.Unlock()\n\tl.refill()\n\treturn int(l.tokens)\n}\n\nfunc (l *Limiter) refill() {\n\tnow := l.clock.Now()\n\tif now.Before(l.last) {\n\t\treturn // clock moved backwards; ignore\n\t}\n\telapsed := now.Sub(l.last).Seconds()\n\tl.tokens += elapsed * l.rate\n\tif l.tokens > l.capacity {\n\t\tl.tokens = l.capacity\n\t}\n\tl.last = now\n}\n```\n\n```go\n// ratelimit/ratelimit_test.go\npackage ratelimit\n\nimport (\n\t\"sync\"\n\t\"testing\"\n\t\"time\"\n)\n\n// fakeClock is a controllable Clock for deterministic tests.\ntype fakeClock struct {\n\tmu sync.Mutex\n\tnow time.Time\n}\n\nfunc newFakeClock(t time.Time) *fakeClock { return &fakeClock{now: t} }\n\nfunc (c *fakeClock) Now() time.Time {\n\tc.mu.Lock()\n\tdefer c.mu.Unlock()\n\treturn c.now\n}\n\nfunc (c *fakeClock) Advance(d time.Duration) {\n\tc.mu.Lock()\n\tdefer c.mu.Unlock()\n\tc.now = c.now.Add(d)\n}\n\nfunc TestNewValidation(t *testing.T) {\n\ttests := []struct {\n\t\tname string\n\t\tcapacity float64\n\t\trate float64\n\t\twantErr bool\n\t}{\n\t\t{\"valid\", 10, 1, false},\n\t\t{\"zero capacity\", 0, 1, true},\n\t\t{\"negative capacity\", -1, 1, true},\n\t\t{\"zero rate\", 10, 0, true},\n\t\t{\"negative rate\", 10, -1, true},\n\t}\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\t_, err := New(tt.capacity, tt.rate, newFakeClock(time.Unix(0, 0)))\n\t\t\tif (err != nil) != tt.wantErr {\n\t\t\t\tt.Fatalf(\"New(%v,%v) err=%v, wantErr=%v\", tt.capacity, tt.rate, err, tt.wantErr)\n\t\t\t}\n\t\t\tif tt.wantErr && err != ErrInvalidConfig {\n\t\t\t\tt.Fatalf(\"expected ErrInvalidConfig, got %v\", err)\n\t\t\t}\n\t\t})\n\t}\n}\n\nfunc TestAllowN(t *testing.T) {\n\tbase := time.Unix(1000, 0)\n\ttests := []struct {\n\t\tname string\n\t\tcapacity float64\n\t\trate float64 // tokens/sec\n\t\tsteps []struct {\n\t\t\tadvance time.Duration\n\t\t\tn int\n\t\t\twant bool\n\t\t\tavail int\n\t\t}\n\t}{\n\t\t{\n\t\t\tname: \"burst then drain\",\n\t\t\tcapacity: 3,\n\t\t\trate: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t\tavail int\n\t\t\t}{\n\t\t\t\t{0, 1, true, 2},\n\t\t\t\t{0, 2, true, 0},\n\t\t\t\t{0, 1, false, 0}, // bucket empty, no time passed\n\t\t\t\t{2 * time.Second, 1, true, 1}, // 2 tokens refilled, 1 consumed\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"capacity caps refill\",\n\t\t\tcapacity: 2,\n\t\t\trate: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t\tavail int\n\t\t\t}{\n\t\t\t\t{0, 2, true, 0},\n\t\t\t\t{10 * time.Second, 2, true, 0}, // refilled to cap=2, consumed\n\t\t\t\t{10 * time.Second, 3, false, 2}, // still capped at 2\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"fractional accumulation\",\n\t\t\tcapacity: 5,\n\t\t\trate: 0.5, // 1 token per 2s\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t\tavail int\n\t\t\t}{\n\t\t\t\t{0, 5, true, 0},\n\t\t\t\t{1 * time.Second, 1, false, 0}, // 0.5 token, floor -> 0\n\t\t\t\t{1 * time.Second, 1, true, 0}, // now 1.0 token, consumed\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"non positive n always allowed\",\n\t\t\tcapacity: 1,\n\t\t\trate: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t\tavail int\n\t\t\t}{\n\t\t\t\t{0, 0, true, 1},\n\t\t\t\t{0, -3, true, 1},\n\t\t\t},\n\t\t},\n\t}\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\tfc := newFakeClock(base)\n\t\t\tl, err := New(tt.capacity, tt.rate, fc)\n\t\t\tif err != nil {\n\t\t\t\tt.Fatalf(\"New: %v\", err)\n\t\t\t}\n\t\t\tfor i, s := range tt.steps {\n\t\t\t\tfc.Advance(s.advance)\n\t\t\t\tif got := l.AllowN(s.n); got != s.want {\n\t\t\t\t\tt.Fatalf(\"step %d: AllowN(%d)=%v, want %v\", i, s.n, got, s.want)\n\t\t\t\t}\n\t\t\t\tif got := l.Available(); got != s.avail {\n\t\t\t\t\tt.Fatalf(\"step %d: Available()=%d, want %d\", i, got, s.avail)\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\t}\n}\n\nfunc TestAllowNConcurrent(t *testing.T) {\n\tfc := newFakeClock(time.Unix(0, 0))\n\tl, err := New(1000, 1000, fc)\n\tif err != nil {\n\t\tt.Fatalf(\"New: %v\", err)\n\t}\n\tvar wg sync.WaitGroup\n\tallowed := make(chan bool, 1000)\n\tfor i := 0; i < 1000; i++ {\n\t\twg.Add(1)\n\t\tgo func() {\n\t\t\tdefer wg.Done()\n\t\t\tallowed <- l.AllowN(1)\n\t\t}()\n\t}\n\twg.Wait()\n\tclose(allowed)\n\tcount := 0\n\tfor a := range allowed {\n\t\tif a {\n\t\t\tcount++\n\t\t}\n\t}\n\tif count != 1000 {\n\t\tt.Fatalf(\"expected 1000 allowed, got %d\", count)\n\t}\n\tif got := l.Available(); got != 0 {\n\t\tt.Fatalf(\"expected 0 remaining, got %d\", got)\n\t}\n}\n```", "output_characters": 5729, "output_sha256": "ffe24e0c662293bcabc764cb04c53cca2e5ae25558c6050464879930d04416e3", "prompt_tokens": 150, "reasoning_characters": 0, "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "response_tail_seconds": 0.076201, "run": 2, "stream_done_marker": true, "stream_sha256": "ffe24e0c662293bcabc764cb04c53cca2e5ae25558c6050464879930d04416e3", "time_to_first_visible_seconds": 0.747572, "time_to_last_output_seconds": 52.08865, "ttft_seconds": 0.747572, "wall_seconds": 52.16485 }, { "cached_prompt_tokens": 0, "completion_tokens": 2179, "completion_validation": { "valid": true }, "decode_seconds": 49.861638, "decode_tokens_per_second": 43.681, "finish_reason": "stop", "measured_sse_events": 751, "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time so tests can drive deterministic scenarios.\ntype Clock interface {\n\tNow() time.Time\n}\n\n// RealClock reports wall-clock time.\ntype RealClock struct{}\n\nfunc (RealClock) Now() time.Time { return time.Now() }\n\n// ErrInvalidConfig is returned by New when arguments are nonsensical.\nvar ErrInvalidConfig = errors.New(\"ratelimit: invalid configuration\")\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\ntype Limiter struct {\n\tmu sync.Mutex\n\tclock Clock\n\tcapacity float64\n\trate float64 // tokens per second\n\ttokens float64\n\tlast time.Time\n\tstartTime time.Time\n}\n\n// New builds a Limiter. capacity must be positive, rate non-negative, and\n// clock non-nil. The bucket starts full.\nfunc New(clock Clock, capacity, rate float64) (*Limiter, error) {\n\tif clock == nil {\n\t\treturn nil, ErrInvalidConfig\n\t}\n\tif capacity <= 0 {\n\t\treturn nil, ErrInvalidConfig\n\t}\n\tif rate < 0 {\n\t\treturn nil, ErrInvalidConfig\n\t}\n\tnow := clock.Now()\n\treturn &Limiter{\n\t\tclock: clock,\n\t\tcapacity: capacity,\n\t\trate: rate,\n\t\ttokens: capacity,\n\t\tlast: now,\n\t\tstartTime: now,\n\t}, nil\n}\n\n// AllowN reports whether n tokens are available, consuming them if so.\n// n <= 0 is always allowed and consumes nothing.\nfunc (l *Limiter) AllowN(n int) bool {\n\tif n <= 0 {\n\t\treturn true\n\t}\n\tl.mu.Lock()\n\tdefer l.mu.Unlock()\n\tl.refill()\n\tif l.tokens < float64(n) {\n\t\treturn false\n\t}\n\tl.tokens -= float64(n)\n\treturn true\n}\n\n// Available returns the current token count, rounded down.\nfunc (l *Limiter) Available() int {\n\tl.mu.Lock()\n\tdefer l.mu.Unlock()\n\tl.refill()\n\treturn int(l.tokens)\n}\n\nfunc (l *Limiter) refill() {\n\tnow := l.clock.Now()\n\tif now.Before(l.last) {\n\t\treturn // clock moved backwards; ignore\n\t}\n\telapsed := now.Sub(l.last).Seconds()\n\tl.last = now\n\tl.tokens += elapsed * l.rate\n\tif l.tokens > l.capacity {\n\t\tl.tokens = l.capacity\n\t}\n}\n```\n\n```go\n// ratelimit_test.go\npackage ratelimit\n\nimport (\n\t\"sync\"\n\t\"testing\"\n\t\"time\"\n)\n\n// fakeClock is a controllable Clock for deterministic tests.\ntype fakeClock struct {\n\tmu sync.Mutex\n\tnow time.Time\n}\n\nfunc newFakeClock(t time.Time) *fakeClock { return &fakeClock{now: t} }\n\nfunc (c *fakeClock) Now() time.Time {\n\tc.mu.Lock()\n\tdefer c.mu.Unlock()\n\treturn c.now\n}\n\nfunc (c *fakeClock) Advance(d time.Duration) {\n\tc.mu.Lock()\n\tdefer c.mu.Unlock()\n\tc.now = c.now.Add(d)\n}\n\nfunc TestNewValidation(t *testing.T) {\n\tbase := time.Date(2024, 1, 1, 0, 0, 0, 0, time.UTC)\n\ttests := []struct {\n\t\tname string\n\t\tclock Clock\n\t\tcapacity float64\n\t\trate float64\n\t\twantErr bool\n\t}{\n\t\t{\"valid\", newFakeClock(base), 5, 1, false},\n\t\t{\"nil clock\", nil, 5, 1, true},\n\t\t{\"zero capacity\", newFakeClock(base), 0, 1, true},\n\t\t{\"negative capacity\", newFakeClock(base), -1, 1, true},\n\t\t{\"negative rate\", newFakeClock(base), 5, -1, true},\n\t\t{\"zero rate ok\", newFakeClock(base), 5, 0, false},\n\t}\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\t_, err := New(tt.clock, tt.capacity, tt.rate)\n\t\t\tif (err != nil) != tt.wantErr {\n\t\t\t\tt.Fatalf(\"New() err=%v wantErr=%v\", err, tt.wantErr)\n\t\t\t}\n\t\t})\n\t}\n}\n\nfunc TestAllowN(t *testing.T) {\n\tbase := time.Date(2024, 1, 1, 0, 0, 0, 0, time.UTC)\n\ttests := []struct {\n\t\tname string\n\t\tcapacity float64\n\t\trate float64 // tokens per second\n\t\tsteps []struct {\n\t\t\tadvance time.Duration\n\t\t\tn int\n\t\t\twant bool\n\t\t}\n\t}{\n\t\t{\n\t\t\tname: \"burst then drain\",\n\t\t\tcapacity: 3,\n\t\t\trate: 0,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 1, true},\n\t\t\t\t{0, 2, true},\n\t\t\t\t{0, 1, false},\n\t\t\t\t{time.Second, 1, false}, // rate 0: no refill\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"refill caps at capacity\",\n\t\t\tcapacity: 2,\n\t\t\trate: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 2, true}, // drain\n\t\t\t\t{3 * time.Second, 2, true}, // refilled to cap 2, not 5\n\t\t\t\t{0, 1, false},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"partial refill\",\n\t\t\tcapacity: 10,\n\t\t\trate: 2,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 10, true}, // drain\n\t\t\t\t{500 * time.Millisecond, 1, true}, // +1 token\n\t\t\t\t{0, 1, false},\n\t\t\t\t{500 * time.Millisecond, 1, true}, // +1 more\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"nonpositive n always allowed\",\n\t\t\tcapacity: 1,\n\t\t\trate: 0,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 0, true},\n\t\t\t\t{0, -3, true},\n\t\t\t\t{0, 1, true},\n\t\t\t\t{0, 1, false},\n\t\t\t},\n\t\t},\n\t}\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\tfc := newFakeClock(base)\n\t\t\tl, err := New(fc, tt.capacity, tt.rate)\n\t\t\tif err != nil {\n\t\t\t\tt.Fatalf(\"New(): %v\", err)\n\t\t\t}\n\t\t\tfor i, s := range tt.steps {\n\t\t\t\tfc.Advance(s.advance)\n\t\t\t\tif got := l.AllowN(s.n); got != s.want {\n\t\t\t\t\tt.Fatalf(\"step %d: AllowN(%d)=%v want %v\", i, s.n, got, s.want)\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\t}\n}\n\nfunc TestAllowNConcurrent(t *testing.T) {\n\tfc := newFakeClock(time.Now())\n\tl, err := New(fc, 1000, 1000)\n\tif err != nil {\n\t\tt.Fatalf(\"New(): %v\", err)\n\t}\n\tvar wg sync.WaitGroup\n\tallowed := make(chan bool, 100)\n\tfor i := 0; i < 100; i++ {\n\t\twg.Add(1)\n\t\tgo func() {\n\t\t\tdefer wg.Done()\n\t\t\tallowed <- l.AllowN(1)\n\t\t}()\n\t}\n\twg.Wait()\n\tclose(allowed)\n\tcount := 0\n\tfor a := range allowed {\n\t\tif a {\n\t\t\tcount++\n\t\t}\n\t}\n\tif count != 100 {\n\t\tt.Fatalf(\"expected all 100 goroutines allowed, got %d\", count)\n\t}\n\tif got := l.Available(); got != 900 {\n\t\tt.Fatalf(\"Available()=%d want 900\", got)\n\t}\n}\n```", "output_characters": 5539, "output_sha256": "8b56ee7655d9089dc6efac6b42504bbfc66184530a3ff8387cdca107922dbe91", "prompt_tokens": 150, "reasoning_characters": 0, "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "response_tail_seconds": 0.075924, "run": 3, "stream_done_marker": true, "stream_sha256": "8b56ee7655d9089dc6efac6b42504bbfc66184530a3ff8387cdca107922dbe91", "time_to_first_visible_seconds": 0.729625, "time_to_last_output_seconds": 50.591263, "ttft_seconds": 0.729625, "wall_seconds": 50.667187 }, { "cached_prompt_tokens": 0, "completion_tokens": 2011, "completion_validation": { "valid": true }, "decode_seconds": 45.721163, "decode_tokens_per_second": 43.962, "finish_reason": "stop", "measured_sse_events": 692, "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock is an injectable source of monotonic wall time.\ntype Clock interface {\n\tNow() time.Time\n}\n\n// RealClock returns the actual current time.\ntype RealClock struct{}\n\nfunc (RealClock) Now() time.Time { return time.Now() }\n\n// ErrInvalidConfig is returned by New when the configuration is invalid.\nvar ErrInvalidConfig = errors.New(\"ratelimit: invalid configuration\")\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\ntype Limiter struct {\n\tmu sync.Mutex\n\tclock Clock\n\tcapacity float64\n\trate float64 // tokens added per nanosecond\n\ttokens float64\n\tlast time.Time\n\tstartTime time.Time\n}\n\n// New creates a Limiter with the given bucket capacity and refill rate.\n// capacity must be positive, rate must be non-negative, and clock must be non-nil.\nfunc New(clock Clock, capacity, rate float64) (*Limiter, error) {\n\tif clock == nil {\n\t\treturn nil, ErrInvalidConfig\n\t}\n\tif capacity <= 0 {\n\t\treturn nil, ErrInvalidConfig\n\t}\n\tif rate < 0 {\n\t\treturn nil, ErrInvalidConfig\n\t}\n\tnow := clock.Now()\n\treturn &Limiter{\n\t\tclock: clock,\n\t\tcapacity: capacity,\n\t\trate: rate / float64(time.Second),\n\t\ttokens: capacity,\n\t\tlast: now,\n\t\tstartTime: now,\n\t}, nil\n}\n\n// AllowN reports whether n tokens are available, consuming them if so.\n// n must be positive; otherwise it returns false.\nfunc (l *Limiter) AllowN(n int) bool {\n\tif n <= 0 {\n\t\treturn false\n\t}\n\tl.mu.Lock()\n\tdefer l.mu.Unlock()\n\tl.advance()\n\tif l.tokens < float64(n) {\n\t\treturn false\n\t}\n\tl.tokens -= float64(n)\n\treturn true\n}\n\n// advance refills the bucket based on elapsed time, clamping to capacity.\nfunc (l *Limiter) advance() {\n\tnow := l.clock.Now()\n\tif now.Before(l.last) {\n\t\treturn // guard against clock going backwards\n\t}\n\telapsed := now.Sub(l.last)\n\tl.tokens += float64(elapsed) * l.rate\n\tif l.tokens > l.capacity {\n\t\tl.tokens = l.capacity\n\t}\n\tl.last = now\n}\n```\n\n```go\n// ratelimit_test.go\npackage ratelimit\n\nimport (\n\t\"sync\"\n\t\"testing\"\n\t\"time\"\n)\n\n// fakeClock is a controllable Clock for deterministic tests.\ntype fakeClock struct {\n\tmu sync.Mutex\n\tnow time.Time\n}\n\nfunc newFakeClock(t time.Time) *fakeClock { return &fakeClock{now: t} }\n\nfunc (c *fakeClock) Now() time.Time {\n\tc.mu.Lock()\n\tdefer c.mu.Unlock()\n\treturn c.now\n}\n\nfunc (c *fakeClock) Advance(d time.Duration) {\n\tc.mu.Lock()\n\tdefer c.mu.Unlock()\n\tc.now = c.now.Add(d)\n}\n\nfunc TestNewValidation(t *testing.T) {\n\tbase := time.Date(2024, 1, 1, 0, 0, 0, 0, time.UTC)\n\tclock := newFakeClock(base)\n\ttests := []struct {\n\t\tname string\n\t\tclock Clock\n\t\tcapacity float64\n\t\trate float64\n\t\twantErr bool\n\t}{\n\t\t{\"valid\", clock, 5, 1, false},\n\t\t{\"nil clock\", nil, 5, 1, true},\n\t\t{\"zero capacity\", clock, 0, 1, true},\n\t\t{\"negative capacity\", clock, -1, 1, true},\n\t\t{\"negative rate\", clock, 5, -1, true},\n\t\t{\"zero rate ok\", clock, 5, 0, false},\n\t}\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\t_, err := New(tt.clock, tt.capacity, tt.rate)\n\t\t\tif (err != nil) != tt.wantErr {\n\t\t\t\tt.Fatalf(\"New() err = %v, wantErr %v\", err, tt.wantErr)\n\t\t\t}\n\t\t})\n\t}\n}\n\nfunc TestAllowN(t *testing.T) {\n\tbase := time.Date(2024, 1, 1, 0, 0, 0, 0, time.UTC)\n\ttests := []struct {\n\t\tname string\n\t\tcapacity float64\n\t\trate float64 // tokens per second\n\t\tsteps []struct {\n\t\t\tadvance time.Duration\n\t\t\tn int\n\t\t\twant bool\n\t\t}\n\t}{\n\t\t{\n\t\t\tname: \"burst then refill\",\n\t\t\tcapacity: 3,\n\t\t\trate: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 1, true},\n\t\t\t\t{0, 3, false}, // only 2 left\n\t\t\t\t{0, 2, true}, // bucket empty\n\t\t\t\t{0, 1, false},\n\t\t\t\t{time.Second, 1, true}, // 1 token refilled\n\t\t\t\t{2 * time.Second, 3, false},\n\t\t\t\t{2 * time.Second, 2, true}, // clamped at capacity 3, 2 consumed\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"zero rate no refill\",\n\t\t\tcapacity: 2,\n\t\t\trate: 0,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 2, true},\n\t\t\t\t{10 * time.Second, 1, false},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"non positive n\",\n\t\t\tcapacity: 2,\n\t\t\trate: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 0, false},\n\t\t\t\t{0, -1, false},\n\t\t\t\t{0, 1, true},\n\t\t\t},\n\t\t},\n\t}\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\tclock := newFakeClock(base)\n\t\t\tl, err := New(clock, tt.capacity, tt.rate)\n\t\t\tif err != nil {\n\t\t\t\tt.Fatalf(\"New() unexpected error: %v\", err)\n\t\t\t}\n\t\t\tfor i, s := range tt.steps {\n\t\t\t\tclock.Advance(s.advance)\n\t\t\t\tif got := l.AllowN(s.n); got != s.want {\n\t\t\t\t\tt.Fatalf(\"step %d: AllowN(%d) = %v, want %v\", i, s.n, got, s.want)\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\t}\n}\n\nfunc TestAllowNConcurrent(t *testing.T) {\n\tclock := newFakeClock(time.Now())\n\tl, err := New(clock, 1000, 1000)\n\tif err != nil {\n\t\tt.Fatalf(\"New() unexpected error: %v\", err)\n\t}\n\tvar wg sync.WaitGroup\n\tvar allowed int64\n\tvar mu sync.Mutex\n\tfor i := 0; i < 50; i++ {\n\t\twg.Add(1)\n\t\tgo func() {\n\t\t\tdefer wg.Done()\n\t\t\tfor j := 0; j < 20; j++ {\n\t\t\t\tif l.AllowN(1) {\n\t\t\t\t\tmu.Lock()\n\t\t\t\t\tallowed++\n\t\t\t\t\tmu.Unlock()\n\t\t\t\t}\n\t\t\t}\n\t\t}()\n\t}\n\twg.Wait()\n\tif allowed > 1000 {\n\t\tt.Fatalf(\"allowed %d exceeds capacity 1000\", allowed)\n\t}\n}\n```", "output_characters": 5194, "output_sha256": "e529e449e12476951e1be684550a83730325707e957e69e6d378c9b5a610d28d", "prompt_tokens": 150, "reasoning_characters": 0, "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "response_tail_seconds": 0.075099, "run": 4, "stream_done_marker": true, "stream_sha256": "e529e449e12476951e1be684550a83730325707e957e69e6d378c9b5a610d28d", "time_to_first_visible_seconds": 0.757135, "time_to_last_output_seconds": 46.478297, "ttft_seconds": 0.757135, "wall_seconds": 46.553396 }, { "cached_prompt_tokens": 0, "completion_tokens": 2658, "completion_validation": { "valid": true }, "decode_seconds": 60.008036, "decode_tokens_per_second": 44.277, "finish_reason": "stop", "measured_sse_events": 907, "output": "```go\n// ratelimit/ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock is an injectable source of monotonic wall time.\ntype Clock interface {\n\tNow() time.Time\n}\n\n// RealClock returns the current time from time.Now.\ntype RealClock struct{}\n\n// Now implements Clock.\nfunc (RealClock) Now() time.Time { return time.Now() }\n\n// ErrInvalidConfig is returned by NewTokenBucket for invalid arguments.\nvar ErrInvalidConfig = errors.New(\"ratelimit: invalid configuration\")\n\n// TokenBucket is a concurrency-safe token-bucket rate limiter.\ntype TokenBucket struct {\n\tmu sync.Mutex\n\tclock Clock\n\tcapacity float64\n\trate float64 // tokens per second\n\ttokens float64\n\tlast time.Time\n\tlastAllow time.Time\n}\n\n// NewTokenBucket creates a limiter that holds up to capacity tokens and\n// refills at rate tokens per second. capacity and rate must be positive,\n// clock must be non-nil, and initialTokens must be within [0, capacity].\nfunc NewTokenBucket(clock Clock, capacity, rate, initialTokens float64) (*TokenBucket, error) {\n\tif clock == nil {\n\t\treturn nil, ErrInvalidConfig\n\t}\n\tif capacity <= 0 || rate <= 0 {\n\t\treturn nil, ErrInvalidConfig\n\t}\n\tif initialTokens < 0 || initialTokens > capacity {\n\t\treturn nil, ErrInvalidConfig\n\t}\n\tnow := clock.Now()\n\treturn &TokenBucket{\n\t\tclock: clock,\n\t\tcapacity: capacity,\n\t\trate: rate,\n\t\ttokens: initialTokens,\n\t\tlast: now,\n\t}, nil\n}\n\n// AllowN reports whether n tokens are available now, consuming them if so.\n// It returns false for n <= 0 or n > capacity without mutating state.\nfunc (b *TokenBucket) AllowN(n int) bool {\n\tif n <= 0 {\n\t\treturn false\n\t}\n\tb.mu.Lock()\n\tdefer b.mu.Unlock()\n\tb.refill()\n\tif float64(n) > b.capacity || float64(n) > b.tokens {\n\t\treturn false\n\t}\n\tb.tokens -= float64(n)\n\tb.lastAllow = b.clock.Now()\n\treturn true\n}\n\n// refill advances the token count based on elapsed time. Callers hold b.mu.\nfunc (b *TokenBucket) refill() {\n\tnow := b.clock.Now()\n\tif !now.After(b.last) {\n\t\treturn\n\t}\n\telapsed := now.Sub(b.last).Seconds()\n\tb.last = now\n\tb.tokens += elapsed * b.rate\n\tif b.tokens > b.capacity {\n\t\tb.tokens = b.capacity\n\t}\n}\n\n// Available returns the current token count, rounded down.\nfunc (b *TokenBucket) Available() int {\n\tb.mu.Lock()\n\tdefer b.mu.Unlock()\n\tb.refill()\n\tif b.tokens < 0 {\n\t\treturn 0\n\t}\n\treturn int(b.tokens)\n}\n```\n\n```go\n// ratelimit/ratelimit_test.go\npackage ratelimit\n\nimport (\n\t\"sync\"\n\t\"testing\"\n\t\"time\"\n)\n\n// fakeClock is a deterministic Clock for tests.\ntype fakeClock struct {\n\tmu sync.Mutex\n\tnow time.Time\n}\n\nfunc newFakeClock(t time.Time) *fakeClock { return &fakeClock{now: t} }\n\nfunc (c *fakeClock) Now() time.Time {\n\tc.mu.Lock()\n\tdefer c.mu.Unlock()\n\treturn c.now\n}\n\nfunc (c *fakeClock) Advance(d time.Duration) {\n\tc.mu.Lock()\n\tdefer c.mu.Unlock()\n\tc.now = c.now.Add(d)\n}\n\nfunc TestNewTokenBucketValidation(t *testing.T) {\n\tbase := time.Date(2024, 1, 1, 0, 0, 0, 0, time.UTC)\n\tclock := newFakeClock(base)\n\ttests := []struct {\n\t\tname string\n\t\tclock Clock\n\t\tcapacity float64\n\t\trate float64\n\t\tinitial float64\n\t\twantErr bool\n\t}{\n\t\t{\"valid\", clock, 10, 1, 5, false},\n\t\t{\"nil clock\", nil, 10, 1, 0, true},\n\t\t{\"zero capacity\", clock, 0, 1, 0, true},\n\t\t{\"negative capacity\", clock, -1, 1, 0, true},\n\t\t{\"zero rate\", clock, 10, 0, 0, true},\n\t\t{\"negative rate\", clock, 10, -1, 0, true},\n\t\t{\"initial negative\", clock, 10, 1, -1, true},\n\t\t{\"initial over capacity\", clock, 10, 1, 11, true},\n\t\t{\"initial at capacity\", clock, 10, 1, 10, false},\n\t}\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\tb, err := NewTokenBucket(tt.clock, tt.capacity, tt.rate, tt.initial)\n\t\t\tif tt.wantErr {\n\t\t\t\tif err == nil {\n\t\t\t\t\tt.Fatalf(\"expected error, got bucket %+v\", b)\n\t\t\t\t}\n\t\t\t\tif !errorsIs(err, ErrInvalidConfig) {\n\t\t\t\t\tt.Fatalf(\"expected ErrInvalidConfig, got %v\", err)\n\t\t\t\t}\n\t\t\t\treturn\n\t\t\t}\n\t\t\tif err != nil {\n\t\t\t\tt.Fatalf(\"unexpected error: %v\", err)\n\t\t\t}\n\t\t\tif b == nil {\n\t\t\t\tt.Fatal(\"expected non-nil bucket\")\n\t\t\t}\n\t\t})\n\t}\n}\n\nfunc errorsIs(err, target error) bool { return err == target }\n\nfunc TestAllowN(t *testing.T) {\n\tbase := time.Date(2024, 1, 1, 0, 0, 0, 0, time.UTC)\n\ttests := []struct {\n\t\tname string\n\t\tcapacity float64\n\t\trate float64\n\t\tinitial float64\n\t\tsteps []struct {\n\t\t\tadvance time.Duration\n\t\t\tn int\n\t\t\twant bool\n\t\t\twantAvl int\n\t\t}\n\t}{\n\t\t{\n\t\t\tname: \"burst then refill\",\n\t\t\tcapacity: 3,\n\t\t\trate: 1,\n\t\t\tinitial: 3,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t\twantAvl int\n\t\t\t}{\n\t\t\t\t{0, 1, true, 2},\n\t\t\t\t{0, 2, true, 0},\n\t\t\t\t{0, 1, false, 0},\n\t\t\t\t{1500 * time.Millisecond, 1, true, 0},\n\t\t\t\t{0, 1, false, 0},\n\t\t\t\t{2 * time.Second, 2, true, 0},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"cap at capacity\",\n\t\t\tcapacity: 2,\n\t\t\trate: 1,\n\t\t\tinitial: 0,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t\twantAvl int\n\t\t\t}{\n\t\t\t\t{5 * time.Second, 2, true, 0},\n\t\t\t\t{5 * time.Second, 1, true, 0},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"invalid n\",\n\t\t\tcapacity: 5,\n\t\t\trate: 1,\n\t\t\tinitial: 5,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t\twantAvl int\n\t\t\t}{\n\t\t\t\t{0, 0, false, 5},\n\t\t\t\t{0, -3, false, 5},\n\t\t\t\t{0, 6, false, 5},\n\t\t\t\t{0, 5, true, 0},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"no backward time\",\n\t\t\tcapacity: 1,\n\t\t\trate: 1,\n\t\t\tinitial: 0,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t\twantAvl int\n\t\t\t}{\n\t\t\t\t{1 * time.Second, 1, true, 0},\n\t\t\t\t{0, 1, false, 0},\n\t\t\t},\n\t\t},\n\t}\n\tfor _, tt := range tests {\n\t\tt.Run(tt.name, func(t *testing.T) {\n\t\t\tclock := newFakeClock(base)\n\t\t\tb, err := NewTokenBucket(clock, tt.capacity, tt.rate, tt.initial)\n\t\t\tif err != nil {\n\t\t\t\tt.Fatalf(\"NewTokenBucket: %v\", err)\n\t\t\t}\n\t\t\tfor i, s := range tt.steps {\n\t\t\t\tclock.Advance(s.advance)\n\t\t\t\tif got := b.AllowN(s.n); got != s.want {\n\t\t\t\t\tt.Fatalf(\"step %d: AllowN(%d) = %v, want %v\", i, s.n, got, s.want)\n\t\t\t\t}\n\t\t\t\tif got := b.Available(); got != s.wantAvl {\n\t\t\t\t\tt.Fatalf(\"step %d: Available() = %d, want %d\", i, got, s.wantAvl)\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\t}\n}\n\nfunc TestAllowNConcurrent(t *testing.T) {\n\tclock := newFakeClock(time.Date(2024, 1, 1, 0, 0, 0, 0, time.UTC))\n\tb, err := NewTokenBucket(clock, 1000, 1000, 1000)\n\tif err != nil {\n\t\tt.Fatalf(\"NewTokenBucket: %v\", err)\n\t}\n\tvar wg sync.WaitGroup\n\tallowed := make(chan bool, 1000)\n\tfor i := 0; i < 1000; i++ {\n\t\twg.Add(1)\n\t\tgo func() {\n\t\t\tdefer wg.Done()\n\t\t\tallowed <- b.AllowN(1)\n\t\t}()\n\t}\n\twg.Wait()\n\tclose(allowed)\n\tcount := 0\n\tfor ok := range allowed {\n\t\tif ok {\n\t\t\tcount++\n\t\t}\n\t}\n\tif count != 1000 {\n\t\tt.Fatalf(\"expected 1000 allowed, got %d\", count)\n\t}\n\tif got := b.Available(); got != 0 {\n\t\tt.Fatalf(\"expected 0 remaining, got %d\", got)\n\t}\n}\n```", "output_characters": 6697, "output_sha256": "b34cf720a05ed82fe7bdd1ff06f63b22ebba3377a1a87242bd4763d5018c12f1", "prompt_tokens": 148, "reasoning_characters": 0, "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "response_tail_seconds": 0.067508, "run": 5, "stream_done_marker": true, "stream_sha256": "b34cf720a05ed82fe7bdd1ff06f63b22ebba3377a1a87242bd4763d5018c12f1", "time_to_first_visible_seconds": 0.747836, "time_to_last_output_seconds": 60.755872, "ttft_seconds": 0.747836, "wall_seconds": 60.823381 } ], "time_to_last_output_seconds": { "maximum": 60.756, "median": 52.089, "minimum": 46.478, "p90": 60.756 }, "ttft_seconds": { "maximum": 0.757, "median": 0.748, "minimum": 0.71, "p90": 0.757 }, "wall_seconds": { "maximum": 60.823, "median": 52.165, "minimum": 46.553, "p90": 60.823 } }, "prose": { "completion_gate": { "passed": 5, "total": 5 }, "decode_tokens_per_second": { "maximum": 33.829, "median": 32.486, "minimum": 30.908, "p90": 33.829 }, "pooled_decode_tokens_per_second": 32.472, "runs": [ { "cached_prompt_tokens": 0, "completion_tokens": 910, "completion_validation": { "valid": true }, "decode_seconds": 27.60305, "decode_tokens_per_second": 32.931, "finish_reason": "stop", "measured_sse_events": 471, "output": "To: Engineering Leadership\nFrom: Infrastructure Working Group\nSubject: Local AI Capacity \u2014 Buy More or Use Better?\n\nWe are at a decision point about how to meet growing demand for local AI inference. The straightforward option is to purchase additional hardware, which would add capacity quickly and reduce the pressure we feel on our current clusters. The less obvious option is to invest in improving how we use the machines we already own. Both paths are defensible, but they differ sharply in cost profile, latency behaviour, reliability, opportunity cost, and how easily we can change our minds later. This memo argues that we should prioritise utilisation improvements now, while keeping a narrow, well-scoped path to incremental purchases if the evidence demands it.\n\nThe financial case for caution is not simply that new hardware is expensive, though it is. It is that buying locks in a fixed cost long before we understand our real workload shape. Additional accelerators bring not only purchase price but also power, cooling, rack space, and a multi-year depreciation commitment. If our demand forecast is even modestly wrong, we will be paying for idle capacity that still consumes energy and floor space. Improving utilisation, by contrast, mostly costs engineering time. That time is real and not free, but it is reversible in a way that a capital purchase is not. We can tune batching policies, tighten scheduling, retire low-value workloads, and reclaim headroom without signing a purchase order we cannot easily unwind.\n\nLatency is the strongest argument on the other side. A utilisation-first strategy can degrade tail latency if we push existing hardware closer to saturation. Careful work can mitigate this, but not eliminate it. If our most important user-facing paths are already approaching their latency budgets, adding hardware may be the only way to restore comfortable margins. We should be honest that this is an empirical question, not a philosophical one. Before buying, we need a clear picture of which services are latency-critical, which are batch-tolerant, and where queuing delays are actually hurting users rather than merely making dashboards look uncomfortable. If the data shows that our pain is concentrated in a small number of high-priority flows, a targeted purchase may be justified. If the pain is diffuse, better scheduling and workload placement will likely help more.\n\nReliability cuts in both directions. Running hardware hotter and closer to its limits can increase failure rates and make maintenance windows more disruptive. On the other hand, a larger fleet is harder to keep healthy, and more machines mean more components that can fail, more firmware to track, and more operational surface area. Adding capacity does not automatically make the system more dependable; it changes the failure modes. What we should not do is pretend that either path removes operational risk. The safer posture is to improve observability and fault isolation on what we have, so that we can tell the difference between a utilisation problem and a genuine hardware constraint.\n\nThe opportunity cost deserves explicit attention. Every engineer-week spent on capacity work is a week not spent on product features, model quality, or reliability engineering elsewhere. A utilisation programme is not a distraction from real work; it is real work, and it competes for the same scarce attention. But it also builds durable capability. Better scheduling, clearer service-level objectives, and sharper visibility into resource consumption will pay off regardless of whether we eventually buy more hardware. A purchase, by contrast, buys time but not understanding. If we buy first and investigate later, we risk solving the wrong problem and still needing the engineering work afterwards.\n\nReversibility is the quiet tiebreaker. We can improve utilisation, measure the results, and still buy hardware in a quarter if the data supports it. We cannot easily sell a rack of accelerators once it is installed and configured. In an environment where demand is growing but its shape is still uncertain, preserving optionality has real value. A small, reversible pilot purchase is not off the table, but it should follow evidence, not precede it.\n\nOur recommendation is therefore to fund a focused utilisation effort for the next quarter, with explicit success criteria around latency tails, queue depth, and cost per inference. We should treat new hardware as a contingent decision, triggered by clear thresholds rather than by discomfort. We acknowledge genuine uncertainty here. Our forecasts may be wrong in either direction, and we may discover that our workloads are fundamentally capacity-bound rather than efficiency-bound. The point is to structure our choices so that being wrong is cheap, and so that we learn before we commit.", "output_characters": 4856, "output_sha256": "0f271b4d0d36a1200f3e8fb2cbad29547c85e2e12ef2d92f63b16cbeece3ec08", "prompt_tokens": 137, "reasoning_characters": 0, "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "response_tail_seconds": 0.073325, "run": 1, "stream_done_marker": true, "stream_sha256": "0f271b4d0d36a1200f3e8fb2cbad29547c85e2e12ef2d92f63b16cbeece3ec08", "time_to_first_visible_seconds": 0.688393, "time_to_last_output_seconds": 28.291443, "ttft_seconds": 0.688393, "wall_seconds": 28.364768 }, { "cached_prompt_tokens": 0, "completion_tokens": 912, "completion_validation": { "valid": true }, "decode_seconds": 26.92955, "decode_tokens_per_second": 33.829, "finish_reason": "stop", "measured_sse_events": 454, "output": "To the engineering team,\n\nWe are at a decision point that will shape our capacity for the next several quarters: whether to purchase additional local AI hardware or to invest our effort in improving the utilisation of the machines we already own. Both paths are defensible, and neither is free of risk, so I want to lay out the trade-offs as clearly as I can before we commit.\n\nThe most immediate argument for buying more hardware is simplicity. Additional nodes increase raw throughput, reduce queueing delays, and give us headroom for experiments that would otherwise be throttled. In a world where latency directly affects user experience and internal iteration speed, the temptation to solve a capacity problem by throwing silicon at it is understandable. Yet cost is not merely the invoice from the vendor. It includes power, cooling, rack space, and the often overlooked expense of maintaining a larger fleet of machines that must be patched, monitored, and eventually replaced. If we expand the cluster without understanding how we use it today, we risk paying twice: once for the hardware and again for the operational burden that comes with it.\n\nImproving utilisation, by contrast, is a subtler path. It asks us to examine scheduling policies, batch sizes, memory management, and the ways our workloads fragment available resources. There is real potential here. Most systems I have seen run far below their theoretical limits, not because the hardware is inadequate, but because the software stack treats it as an infinite, homogeneous resource. By profiling our jobs, tightening our orchestration, and eliminating idle time, we may recover capacity that we already paid for. The cost of this approach is primarily engineering time, which is itself a scarce resource, but the expense is more flexible and more reversible than a capital purchase.\n\nReliability complicates the picture in both directions. A larger cluster introduces more failure modes: more drives to fail, more network links to degrade, more firmware to update. If our current reliability challenges stem from software contention or resource starvation, adding hardware could mask the symptoms while leaving the root causes intact. On the other hand, if we are already running at the edge of stability and utilisation improvements require deeper changes to our runtime, we may introduce new failure paths in the short term. I do not think either outcome is inevitable, but we should be honest that reliability is not a neutral factor in this decision.\n\nOpportunity cost deserves more attention than it usually receives. The engineering hours we spend on utilisation work are hours we cannot spend on feature development, model improvements, or customer-facing fixes. Conversely, the capital we spend on new hardware is capital we cannot invest in hiring, tooling, or strategic experiments. If we buy hardware and it turns out to be unnecessary, the sunk cost is tangible and the reversal is slow and expensive. If we invest in utilisation and the gains disappoint us, we can stop, reassess, and redirect our effort with far less friction. This asymmetry matters. Reversibility is not a theoretical virtue; it is a practical safeguard against the uncertainty that surrounds our forecasts.\n\nI want to be candid about that uncertainty. We do not have precise measurements of how much idle capacity we could reclaim, and our projections of future demand are educated guesses rather than firm commitments. It is possible that utilisation improvements yield only marginal gains and that we will need new hardware anyway. It is also possible that we discover our current fleet is sufficient and that a purchase would have been a costly mistake. Neither outcome would be embarrassing, but one is far easier to walk back than the other.\n\nMy recommendation is to prioritise utilisation improvements for the next quarter while deferring any hardware purchase. I propose that we dedicate a small, focused effort to profiling, scheduling, and resource management, with clear success criteria and a firm endpoint. If we demonstrate meaningful gains, we will have bought ourselves time and reduced the size of any future purchase. If we do not, we will have a much stronger case for buying hardware, supported by data rather than intuition. This approach respects our budget, preserves our flexibility, and treats the decision as a learning process rather than a single bet.\n\nI am confident this is the right direction, but I am not confident in the magnitude of the results. I would rather move forward with a plan that acknowledges that uncertainty than pretend we can see the future clearly enough to justify a large, irreversible commitment today.\n\nI look forward to discussing this further and refining the plan together.\n\nBest regards,\n[Your name]", "output_characters": 4814, "output_sha256": "1f393543206c26096b687c898b90e752be630b690f870be0db91f44580f21a95", "prompt_tokens": 139, "reasoning_characters": 0, "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "response_tail_seconds": 0.000781, "run": 2, "stream_done_marker": true, "stream_sha256": "1f393543206c26096b687c898b90e752be630b690f870be0db91f44580f21a95", "time_to_first_visible_seconds": 0.769637, "time_to_last_output_seconds": 27.699188, "ttft_seconds": 0.769637, "wall_seconds": 27.699968 }, { "cached_prompt_tokens": 0, "completion_tokens": 911, "completion_validation": { "valid": true }, "decode_seconds": 28.011832, "decode_tokens_per_second": 32.486, "finish_reason": "stop", "measured_sse_events": 473, "output": "To the engineering leadership team,\n\nI want to lay out how I am thinking about our compute question, because I believe the framing matters as much as the answer. The immediate trigger is simple: our local inference capacity feels tight, and the easy reflex is to order more hardware. But before we do that, I think we owe it to the team and the budget to ask whether we are actually using what we already own as well as we could.\n\nThe cost side is not just the sticker price of another rack or accelerator node. It is the fully loaded cost over time, including power, cooling, rack space, maintenance windows, and the engineering hours required to integrate, monitor, and eventually decommission the equipment. Buying more hardware also locks us into a particular generation of silicon, which means we are implicitly betting that this generation will remain cost effective for the workloads we care about. Improving utilisation, by contrast, mostly costs us time and discipline. Better batching, smarter scheduling, more aggressive caching, pruning unused models, and cleaning up long lived experimental jobs all require attention from people we would rather have building features, but they do not commit us to a multi year depreciation curve.\n\nLatency is where the trade offs get interesting. If our current utilisation spikes are causing queuing delays, it is tempting to assume that more hardware will smooth the curve. Sometimes that is true, but often the real problem is that our workloads are poorly shaped, with small requests interleaved in ways that prevent efficient batching, or with models loaded and unloaded in a pattern that thrashes memory. Fixing those issues can reduce tail latency even at high utilisation, because the system spends less time context switching and more time doing useful work. On the other hand, if our demand is genuinely growing and our peaks are structural rather than accidental, then no amount of scheduling cleverness will keep latency within our targets forever. The honest answer is that we do not yet know which regime we are in, and that uncertainty should make us cautious about large capital commitments.\n\nReliability cuts in both directions as well. Adding more hardware increases the surface area for failure, even if it also gives us more redundancy. A larger cluster means more drives, more interconnects, more firmware updates, and more chances for a subtle configuration drift to take down a slice of capacity. Improving utilisation on the existing fleet, if done carefully, can actually make the system more predictable, because we reduce chaotic load patterns and give ourselves clearer baselines. But if we push utilisation too close to the edge without proper headroom, we trade away our ability to absorb failures gracefully. The goal should be a system that is busy but not brittle, and that distinction is easy to lose in an internal debate about numbers.\n\nThe opportunity cost here is not abstract. Every week we spend procuring, racking, and tuning new hardware is a week we are not spending on the roadmap items that depend on this infrastructure being stable. If we can get meaningful additional capacity out of what we already own, we buy ourselves time to understand our real demand profile before making a bigger commitment. That matters, because our forecasts have been wrong before, and I expect them to be wrong again.\n\nReversibility is the lens that finally makes the decision clearer for me. Hardware purchases are hard to reverse. Once we sign the purchase order, we are committed, and if our assumptions about growth or model sizes turn out to be off, we will be stuck managing underutilised assets. Changes to scheduling, batching, and workload hygiene are much easier to roll back or adjust. They also generate information. If we implement better controls and still see sustained saturation, we will have a far stronger case for additional hardware, backed by data rather than anxiety.\n\nMy recommendation, then, is that we hold off on new hardware for now and invest a focused sprint in improving utilisation of our existing fleet. I want us to instrument our workloads more carefully, identify the worst offenders in terms of memory and time, and implement a set of targeted changes to scheduling and model lifecycle management. We should set clear thresholds for latency and utilisation that, if crossed and sustained, trigger a reevaluation of the buy decision. I am not confident this will be enough forever, and I suspect we will eventually need more capacity, but I am confident that this path gives us more information, more flexibility, and a better chance of spending wisely when the time comes.\n\nI am happy to walk through the details in our next meeting.", "output_characters": 4745, "output_sha256": "14518003bc1cb3f610e1210094980ea9375bf2f2a8432f38e2492a5a58d12251", "prompt_tokens": 143, "reasoning_characters": 0, "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "response_tail_seconds": 0.001241, "run": 3, "stream_done_marker": true, "stream_sha256": "14518003bc1cb3f610e1210094980ea9375bf2f2a8432f38e2492a5a58d12251", "time_to_first_visible_seconds": 0.773584, "time_to_last_output_seconds": 28.785415, "ttft_seconds": 0.773584, "wall_seconds": 28.786656 }, { "cached_prompt_tokens": 0, "completion_tokens": 975, "completion_validation": { "valid": true }, "decode_seconds": 31.5126, "decode_tokens_per_second": 30.908, "finish_reason": "stop", "measured_sse_events": 537, "output": "To: Engineering Leadership\nFrom: Infrastructure Working Group\nSubject: Local AI Capacity: Buy More or Use Better\n\nWe are at a decision point about how to handle the growing pressure on our local AI infrastructure. Demand for inference and fine-tuning has been rising steadily, and the instinctive response from several quarters is to purchase additional GPUs. Before we commit capital, I want to lay out the tradeoffs as I see them, because I believe the better path is to improve utilisation of the hardware we already own, at least for the next two quarters.\n\nThe most obvious argument for buying more is cost avoidance in the form of developer time. When engineers queue jobs for hours, they either wait, work around the bottleneck, or quietly spin up cloud instances with their own budgets. All three outcomes are expensive, though only the last shows up clearly in a ledger. Latency matters here in a way that is easy to underestimate. Interactive experimentation feels qualitatively different when a model responds in seconds rather than minutes, and that responsiveness compounds across a team. If our current fleet is saturated during peak hours, adding capacity would smooth that experience immediately.\n\nBut the cost of new hardware is not just the purchase price. It is the rack space, the power provisioning, the cooling headroom we have not formally verified, and the operational burden of integrating new machines into our existing job scheduler and monitoring stack. We would also be locking in a specific generation of silicon at a specific moment, and the reversibility of that decision is poor. A misjudged purchase is not something we can unwind on a quarterly basis; it is a multi-year commitment that will sit in the building whether or not our usage patterns evolve as we expect.\n\nBy contrast, improving utilisation is a decision we can take back. If we invest engineering effort in better batching, smarter queueing, more aggressive memory management, and clearer ownership of long-running jobs, and those measures turn out to be insufficient, we have lost some time but not a large sum of money. We can still buy hardware later from a position of better information. That asymmetry matters. The reversibility of software and configuration work is high, and the option value of waiting is real, particularly when the underlying hardware market is moving quickly and prices per unit of performance continue to shift.\n\nThere is also an opportunity cost that does not appear on any invoice. The engineers who would be unpacking, cabling, and debugging new nodes are the same people who understand our current workload patterns most deeply. Pulling them into a procurement and installation cycle means pulling them away from the model work and tooling improvements that actually differentiate us. If we buy now, we are implicitly choosing to slow down our applied research roadmap in order to accelerate our infrastructure capacity, and I am not convinced that trade is favourable yet.\n\nReliability cuts both ways in this analysis. A larger fleet means more individual components that can fail, more firmware variants to track, and more surface area for subtle thermal or networking issues. Our current setup is small enough that we know its quirks intimately. Expanding it without first tightening our observability and incident response practices would increase mean time to repair even if mean time between failures stays constant. On the other hand, running existing nodes at higher utilisation for longer periods can shorten their effective lifespan and increase the chance that a single failure disrupts a larger share of total capacity. We need to be honest that neither path is risk free.\n\nMy recommendation, then, is to defer any significant hardware purchase and instead fund a focused effort to raise utilisation. That effort should include profiling our jobs to find pathological memory usage, introducing fairer scheduling policies so that a few large experiments do not starve smaller interactive work, and setting clear expectations with teams about what constitutes reasonable resource requests. We should also establish better dashboards so that we can distinguish genuine capacity limits from temporary spikes and poorly shaped workloads.\n\nI want to be explicit about the uncertainty here. Our demand forecasts are rough, our understanding of future model sizes is incomplete, and it is possible that utilisation improvements will yield only a modest bump rather than a step change. If that happens, we may end up buying hardware in a less favourable market later, and some engineers will have spent frustrating weeks working around constraints we could have relieved sooner. I accept that risk, but I believe it is smaller than the risk of committing capital before we understand how much of our current problem is structural inefficiency rather than genuine scarcity.\n\nIn short, I am asking us to treat this as an experiment with a clear decision gate. We give ourselves a bounded period to improve utilisation, measure the results honestly, and only then decide whether additional hardware is justified. That approach respects our budget, preserves our flexibility, and keeps our engineering time pointed at the problems where we can still learn something.", "output_characters": 5296, "output_sha256": "ec9d80702b80ab13eaa530453f30ba52f3e582609fcf9b2fc2a0a72a41a83d43", "prompt_tokens": 142, "reasoning_characters": 0, "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "response_tail_seconds": 0.081831, "run": 4, "stream_done_marker": true, "stream_sha256": "ec9d80702b80ab13eaa530453f30ba52f3e582609fcf9b2fc2a0a72a41a83d43", "time_to_first_visible_seconds": 0.739295, "time_to_last_output_seconds": 32.251895, "ttft_seconds": 0.739295, "wall_seconds": 32.333726 }, { "cached_prompt_tokens": 0, "completion_tokens": 936, "completion_validation": { "valid": true }, "decode_seconds": 28.803911, "decode_tokens_per_second": 32.461, "finish_reason": "stop", "measured_sse_events": 487, "output": "To: Engineering Leadership\nFrom: Infrastructure Working Group\nSubject: Local AI Capacity \u2014 Buy More or Use Better\n\nWe are at a decision point about how to meet growing demand for local inference. The straightforward option is to purchase additional accelerators and expand the rack footprint. The less glamorous option is to examine how the existing fleet is actually being used and to raise utilisation before committing more capital. Both paths are defensible, but they differ sharply in cost profile, latency behaviour, reliability risk, opportunity cost, and how easily we can reverse course. This memo argues that we should pursue utilisation improvements first, while preserving a narrow path to incremental hardware purchases if the data demands it.\n\nThe financial case for restraint is not subtle. New hardware carries a large upfront cost, a multi-year depreciation tail, and ongoing power and cooling expenses that rarely appear in the initial approval. By contrast, improving utilisation mostly costs engineering time, which we already pay for, and can be redirected if it fails to deliver. Even a modest increase in average GPU occupancy can defer a purchase that would otherwise lock us into a fixed capacity assumption at a moment when model sizes, request patterns, and tooling are all still moving. Spending capital now is a bet that today\u2019s workload shape is stable, and we do not have enough evidence to make that bet confidently.\n\nLatency is often cited as the reason to add hardware, but the relationship between capacity and response time is more complicated than it appears. Adding nodes can reduce queueing delay when the system is saturated, yet it can also introduce new variability through uneven load balancing, cold starts, and network hops. Before we assume that more machines will make responses faster, we need to understand where time is actually being spent. If a significant share of latency comes from inefficient batching, suboptimal scheduling, or redundant preprocessing, then additional hardware will simply amplify those inefficiencies at greater scale. Profiling the current stack may reveal that the binding constraint is software, not silicon.\n\nReliability cuts in both directions as well. A larger fleet means more devices to monitor, more firmware to track, and more failure modes to handle. It also increases the blast radius of configuration errors and makes capacity planning more sensitive to individual node degradation. A smaller, better-utilised fleet is easier to operate, easier to reason about, and easier to keep healthy. On the other hand, running existing hardware closer to its limits can raise thermal stress and shorten component lifetimes if we push too hard without proper headroom. The goal should be disciplined utilisation, not reckless saturation, and that distinction matters for how we set thresholds and alerts.\n\nThe opportunity cost of buying hardware now is not just the invoice. It is the engineering work we postpone while we integrate, validate, and babysit new systems. If we instead invest that effort in better scheduling, caching, request coalescing, and observability, we may discover that our perceived capacity problem is partly a measurement problem. We currently lack fine-grained visibility into per-model resource consumption, idle intervals, and retry storms. Closing that gap has value regardless of whether we eventually expand the fleet, because it improves our ability to make any future decision with confidence.\n\nReversibility is the most underweighted factor in this discussion. Software changes to improve utilisation are largely reversible; we can roll back a scheduler tweak, adjust a timeout, or disable an aggressive caching layer with minimal consequence. Hardware purchases are not reversible in any practical sense. Once we commit capital, we are stuck with the asset, the power contract, and the physical space, even if demand shifts or newer architectures arrive. In an environment of genuine uncertainty about model evolution and usage growth, preserving optionality has real economic value.\n\nOur recommendation is therefore to launch a focused utilisation initiative before approving any significant hardware expansion. We should instrument the fleet, identify the top sources of waste, and implement the highest-leverage software improvements over the next quarter. We should simultaneously define clear trigger conditions under which a modest hardware purchase would be justified, such as sustained high occupancy after optimisation, unacceptable tail latency under realistic load, or a concrete new workload that cannot be met otherwise. This approach acknowledges that we may still need more capacity, but it insists that the decision be driven by evidence rather than anxiety.\n\nWe are uncertain about future demand, about how much headroom software changes can unlock, and about how quickly model requirements will evolve. What we are not uncertain about is that buying hardware now would be difficult to undo, while improving utilisation is cheap to try, easy to measure, and valuable even if it only delays a purchase. We recommend that we start there.", "output_characters": 5165, "output_sha256": "bedbb9e7bab9dac744e229945285470ff5b8c165cee9752fd760f60e04a19d42", "prompt_tokens": 139, "reasoning_characters": 0, "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "response_tail_seconds": 0.00064, "run": 5, "stream_done_marker": true, "stream_sha256": "bedbb9e7bab9dac744e229945285470ff5b8c165cee9752fd760f60e04a19d42", "time_to_first_visible_seconds": 0.760577, "time_to_last_output_seconds": 29.564488, "ttft_seconds": 0.760577, "wall_seconds": 29.565127 } ], "time_to_last_output_seconds": { "maximum": 32.252, "median": 28.785, "minimum": 27.699, "p90": 32.252 }, "ttft_seconds": { "maximum": 0.774, "median": 0.761, "minimum": 0.688, "p90": 0.774 }, "wall_seconds": { "maximum": 32.334, "median": 28.787, "minimum": 27.7, "p90": 32.334 } }, "structured": { "completion_gate": { "passed": 5, "total": 5 }, "decode_tokens_per_second": { "maximum": 47.301, "median": 47.079, "minimum": 43.574, "p90": 47.301 }, "pooled_decode_tokens_per_second": 46.526, "runs": [ { "cached_prompt_tokens": 0, "completion_tokens": 852, "completion_validation": { "valid": true }, "decode_seconds": 18.152508, "decode_tokens_per_second": 46.881, "finish_reason": "stop", "measured_sse_events": 262, "output": "[\n {\"index\": 1, \"square\": 1},\n {\"index\": 2, \"square\": 4},\n {\"index\": 3, \"square\": 9},\n {\"index\": 4, \"square\": 16},\n {\"index\": 5, \"square\": 25},\n {\"index\": 6, \"square\": 36},\n {\"index\": 7, \"square\": 49},\n {\"index\": 8, \"square\": 64},\n {\"index\": 9, \"square\": 81},\n {\"index\": 10, \"square\": 100},\n {\"index\": 11, \"square\": 121},\n {\"index\": 12, \"square\": 144},\n {\"index\": 13, \"square\": 169},\n {\"index\": 14, \"square\": 196},\n {\"index\": 15, \"square\": 225},\n {\"index\": 16, \"square\": 256},\n {\"index\": 17, \"square\": 289},\n {\"index\": 18, \"square\": 324},\n {\"index\": 19, \"square\": 361},\n {\"index\": 20, \"square\": 400},\n {\"index\": 21, \"square\": 441},\n {\"index\": 22, \"square\": 484},\n {\"index\": 23, \"square\": 529},\n {\"index\": 24, \"square\": 576},\n {\"index\": 25, \"square\": 625},\n {\"index\": 26, \"square\": 676},\n {\"index\": 27, \"square\": 729},\n {\"index\": 28, \"square\": 784},\n {\"index\": 29, \"square\": 841},\n {\"index\": 30, \"square\": 900},\n {\"index\": 31, \"square\": 961},\n {\"index\": 32, \"square\": 1024},\n {\"index\": 33, \"square\": 1089},\n {\"index\": 34, \"square\": 1156},\n {\"index\": 35, \"square\": 1225},\n {\"index\": 36, \"square\": 1296},\n {\"index\": 37, \"square\": 1369},\n {\"index\": 38, \"square\": 1444},\n {\"index\": 39, \"square\": 1521},\n {\"index\": 40, \"square\": 1600},\n {\"index\": 41, \"square\": 1681},\n {\"index\": 42, \"square\": 1764},\n {\"index\": 43, \"square\": 1849},\n {\"index\": 44, \"square\": 1936},\n {\"index\": 45, \"square\": 2025},\n {\"index\": 46, \"square\": 2116},\n {\"index\": 47, \"square\": 2209},\n {\"index\": 48, \"square\": 2304},\n {\"index\": 49, \"square\": 2401},\n {\"index\": 50, \"square\": 2500}\n]", "output_characters": 1600, "output_sha256": "b357eaecb1f80ddb0c0131e67d4bd0edc32f8df925960270694b2f896b1c8d04", "prompt_tokens": 129, "reasoning_characters": 0, "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "response_tail_seconds": 0.075193, "run": 1, "stream_done_marker": true, "stream_sha256": "b357eaecb1f80ddb0c0131e67d4bd0edc32f8df925960270694b2f896b1c8d04", "time_to_first_visible_seconds": 0.725776, "time_to_last_output_seconds": 18.878284, "ttft_seconds": 0.725776, "wall_seconds": 18.953477 }, { "cached_prompt_tokens": 0, "completion_tokens": 600, "completion_validation": { "valid": true }, "decode_seconds": 13.746837, "decode_tokens_per_second": 43.574, "finish_reason": "stop", "measured_sse_events": 201, "output": "[{\"index\":1,\"square\":1},{\"index\":2,\"square\":4},{\"index\":3,\"square\":9},{\"index\":4,\"square\":16},{\"index\":5,\"square\":25},{\"index\":6,\"square\":36},{\"index\":7,\"square\":49},{\"index\":8,\"square\":64},{\"index\":9,\"square\":81},{\"index\":10,\"square\":100},{\"index\":11,\"square\":121},{\"index\":12,\"square\":144},{\"index\":13,\"square\":169},{\"index\":14,\"square\":196},{\"index\":15,\"square\":225},{\"index\":16,\"square\":256},{\"index\":17,\"square\":289},{\"index\":18,\"square\":324},{\"index\":19,\"square\":361},{\"index\":20,\"square\":400},{\"index\":21,\"square\":441},{\"index\":22,\"square\":484},{\"index\":23,\"square\":529},{\"index\":24,\"square\":576},{\"index\":25,\"square\":625},{\"index\":26,\"square\":676},{\"index\":27,\"square\":729},{\"index\":28,\"square\":784},{\"index\":29,\"square\":841},{\"index\":30,\"square\":900},{\"index\":31,\"square\":961},{\"index\":32,\"square\":1024},{\"index\":33,\"square\":1089},{\"index\":34,\"square\":1156},{\"index\":35,\"square\":1225},{\"index\":36,\"square\":1296},{\"index\":37,\"square\":1369},{\"index\":38,\"square\":1444},{\"index\":39,\"square\":1521},{\"index\":40,\"square\":1600},{\"index\":41,\"square\":1681},{\"index\":42,\"square\":1764},{\"index\":43,\"square\":1849},{\"index\":44,\"square\":1936},{\"index\":45,\"square\":2025},{\"index\":46,\"square\":2116},{\"index\":47,\"square\":2209},{\"index\":48,\"square\":2304},{\"index\":49,\"square\":2401},{\"index\":50,\"square\":2500}]", "output_characters": 1299, "output_sha256": "654a5359e59bad57071f454922f88d55d393a17d6eec293ae79ea185c3cd7453", "prompt_tokens": 127, "reasoning_characters": 0, "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "response_tail_seconds": 0.073524, "run": 2, "stream_done_marker": true, "stream_sha256": "654a5359e59bad57071f454922f88d55d393a17d6eec293ae79ea185c3cd7453", "time_to_first_visible_seconds": 0.533024, "time_to_last_output_seconds": 14.279861, "ttft_seconds": 0.533024, "wall_seconds": 14.353385 }, { "cached_prompt_tokens": 0, "completion_tokens": 852, "completion_validation": { "valid": true }, "decode_seconds": 18.076096, "decode_tokens_per_second": 47.079, "finish_reason": "stop", "measured_sse_events": 261, "output": "[\n {\"index\": 1, \"square\": 1},\n {\"index\": 2, \"square\": 4},\n {\"index\": 3, \"square\": 9},\n {\"index\": 4, \"square\": 16},\n {\"index\": 5, \"square\": 25},\n {\"index\": 6, \"square\": 36},\n {\"index\": 7, \"square\": 49},\n {\"index\": 8, \"square\": 64},\n {\"index\": 9, \"square\": 81},\n {\"index\": 10, \"square\": 100},\n {\"index\": 11, \"square\": 121},\n {\"index\": 12, \"square\": 144},\n {\"index\": 13, \"square\": 169},\n {\"index\": 14, \"square\": 196},\n {\"index\": 15, \"square\": 225},\n {\"index\": 16, \"square\": 256},\n {\"index\": 17, \"square\": 289},\n {\"index\": 18, \"square\": 324},\n {\"index\": 19, \"square\": 361},\n {\"index\": 20, \"square\": 400},\n {\"index\": 21, \"square\": 441},\n {\"index\": 22, \"square\": 484},\n {\"index\": 23, \"square\": 529},\n {\"index\": 24, \"square\": 576},\n {\"index\": 25, \"square\": 625},\n {\"index\": 26, \"square\": 676},\n {\"index\": 27, \"square\": 729},\n {\"index\": 28, \"square\": 784},\n {\"index\": 29, \"square\": 841},\n {\"index\": 30, \"square\": 900},\n {\"index\": 31, \"square\": 961},\n {\"index\": 32, \"square\": 1024},\n {\"index\": 33, \"square\": 1089},\n {\"index\": 34, \"square\": 1156},\n {\"index\": 35, \"square\": 1225},\n {\"index\": 36, \"square\": 1296},\n {\"index\": 37, \"square\": 1369},\n {\"index\": 38, \"square\": 1444},\n {\"index\": 39, \"square\": 1521},\n {\"index\": 40, \"square\": 1600},\n {\"index\": 41, \"square\": 1681},\n {\"index\": 42, \"square\": 1764},\n {\"index\": 43, \"square\": 1849},\n {\"index\": 44, \"square\": 1936},\n {\"index\": 45, \"square\": 2025},\n {\"index\": 46, \"square\": 2116},\n {\"index\": 47, \"square\": 2209},\n {\"index\": 48, \"square\": 2304},\n {\"index\": 49, \"square\": 2401},\n {\"index\": 50, \"square\": 2500}\n]", "output_characters": 1600, "output_sha256": "b357eaecb1f80ddb0c0131e67d4bd0edc32f8df925960270694b2f896b1c8d04", "prompt_tokens": 126, "reasoning_characters": 0, "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "response_tail_seconds": 0.074468, "run": 3, "stream_done_marker": true, "stream_sha256": "b357eaecb1f80ddb0c0131e67d4bd0edc32f8df925960270694b2f896b1c8d04", "time_to_first_visible_seconds": 0.490759, "time_to_last_output_seconds": 18.566855, "ttft_seconds": 0.490759, "wall_seconds": 18.641322 }, { "cached_prompt_tokens": 0, "completion_tokens": 852, "completion_validation": { "valid": true }, "decode_seconds": 18.071855, "decode_tokens_per_second": 47.09, "finish_reason": "stop", "measured_sse_events": 264, "output": "[\n {\"index\": 1, \"square\": 1},\n {\"index\": 2, \"square\": 4},\n {\"index\": 3, \"square\": 9},\n {\"index\": 4, \"square\": 16},\n {\"index\": 5, \"square\": 25},\n {\"index\": 6, \"square\": 36},\n {\"index\": 7, \"square\": 49},\n {\"index\": 8, \"square\": 64},\n {\"index\": 9, \"square\": 81},\n {\"index\": 10, \"square\": 100},\n {\"index\": 11, \"square\": 121},\n {\"index\": 12, \"square\": 144},\n {\"index\": 13, \"square\": 169},\n {\"index\": 14, \"square\": 196},\n {\"index\": 15, \"square\": 225},\n {\"index\": 16, \"square\": 256},\n {\"index\": 17, \"square\": 289},\n {\"index\": 18, \"square\": 324},\n {\"index\": 19, \"square\": 361},\n {\"index\": 20, \"square\": 400},\n {\"index\": 21, \"square\": 441},\n {\"index\": 22, \"square\": 484},\n {\"index\": 23, \"square\": 529},\n {\"index\": 24, \"square\": 576},\n {\"index\": 25, \"square\": 625},\n {\"index\": 26, \"square\": 676},\n {\"index\": 27, \"square\": 729},\n {\"index\": 28, \"square\": 784},\n {\"index\": 29, \"square\": 841},\n {\"index\": 30, \"square\": 900},\n {\"index\": 31, \"square\": 961},\n {\"index\": 32, \"square\": 1024},\n {\"index\": 33, \"square\": 1089},\n {\"index\": 34, \"square\": 1156},\n {\"index\": 35, \"square\": 1225},\n {\"index\": 36, \"square\": 1296},\n {\"index\": 37, \"square\": 1369},\n {\"index\": 38, \"square\": 1444},\n {\"index\": 39, \"square\": 1521},\n {\"index\": 40, \"square\": 1600},\n {\"index\": 41, \"square\": 1681},\n {\"index\": 42, \"square\": 1764},\n {\"index\": 43, \"square\": 1849},\n {\"index\": 44, \"square\": 1936},\n {\"index\": 45, \"square\": 2025},\n {\"index\": 46, \"square\": 2116},\n {\"index\": 47, \"square\": 2209},\n {\"index\": 48, \"square\": 2304},\n {\"index\": 49, \"square\": 2401},\n {\"index\": 50, \"square\": 2500}\n]", "output_characters": 1600, "output_sha256": "b357eaecb1f80ddb0c0131e67d4bd0edc32f8df925960270694b2f896b1c8d04", "prompt_tokens": 130, "reasoning_characters": 0, "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "response_tail_seconds": 0.073839, "run": 4, "stream_done_marker": true, "stream_sha256": "b357eaecb1f80ddb0c0131e67d4bd0edc32f8df925960270694b2f896b1c8d04", "time_to_first_visible_seconds": 0.695467, "time_to_last_output_seconds": 18.767322, "ttft_seconds": 0.695467, "wall_seconds": 18.841161 }, { "cached_prompt_tokens": 0, "completion_tokens": 852, "completion_validation": { "valid": true }, "decode_seconds": 17.991101, "decode_tokens_per_second": 47.301, "finish_reason": "stop", "measured_sse_events": 262, "output": "[\n {\"index\": 1, \"square\": 1},\n {\"index\": 2, \"square\": 4},\n {\"index\": 3, \"square\": 9},\n {\"index\": 4, \"square\": 16},\n {\"index\": 5, \"square\": 25},\n {\"index\": 6, \"square\": 36},\n {\"index\": 7, \"square\": 49},\n {\"index\": 8, \"square\": 64},\n {\"index\": 9, \"square\": 81},\n {\"index\": 10, \"square\": 100},\n {\"index\": 11, \"square\": 121},\n {\"index\": 12, \"square\": 144},\n {\"index\": 13, \"square\": 169},\n {\"index\": 14, \"square\": 196},\n {\"index\": 15, \"square\": 225},\n {\"index\": 16, \"square\": 256},\n {\"index\": 17, \"square\": 289},\n {\"index\": 18, \"square\": 324},\n {\"index\": 19, \"square\": 361},\n {\"index\": 20, \"square\": 400},\n {\"index\": 21, \"square\": 441},\n {\"index\": 22, \"square\": 484},\n {\"index\": 23, \"square\": 529},\n {\"index\": 24, \"square\": 576},\n {\"index\": 25, \"square\": 625},\n {\"index\": 26, \"square\": 676},\n {\"index\": 27, \"square\": 729},\n {\"index\": 28, \"square\": 784},\n {\"index\": 29, \"square\": 841},\n {\"index\": 30, \"square\": 900},\n {\"index\": 31, \"square\": 961},\n {\"index\": 32, \"square\": 1024},\n {\"index\": 33, \"square\": 1089},\n {\"index\": 34, \"square\": 1156},\n {\"index\": 35, \"square\": 1225},\n {\"index\": 36, \"square\": 1296},\n {\"index\": 37, \"square\": 1369},\n {\"index\": 38, \"square\": 1444},\n {\"index\": 39, \"square\": 1521},\n {\"index\": 40, \"square\": 1600},\n {\"index\": 41, \"square\": 1681},\n {\"index\": 42, \"square\": 1764},\n {\"index\": 43, \"square\": 1849},\n {\"index\": 44, \"square\": 1936},\n {\"index\": 45, \"square\": 2025},\n {\"index\": 46, \"square\": 2116},\n {\"index\": 47, \"square\": 2209},\n {\"index\": 48, \"square\": 2304},\n {\"index\": 49, \"square\": 2401},\n {\"index\": 50, \"square\": 2500}\n]", "output_characters": 1600, "output_sha256": "b357eaecb1f80ddb0c0131e67d4bd0edc32f8df925960270694b2f896b1c8d04", "prompt_tokens": 128, "reasoning_characters": 0, "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "response_tail_seconds": 0.073476, "run": 5, "stream_done_marker": true, "stream_sha256": "b357eaecb1f80ddb0c0131e67d4bd0edc32f8df925960270694b2f896b1c8d04", "time_to_first_visible_seconds": 0.491967, "time_to_last_output_seconds": 18.483068, "ttft_seconds": 0.491967, "wall_seconds": 18.556544 } ], "time_to_last_output_seconds": { "maximum": 18.878, "median": 18.567, "minimum": 14.28, "p90": 18.878 }, "ttft_seconds": { "maximum": 0.726, "median": 0.533, "minimum": 0.491, "p90": 0.726 }, "wall_seconds": { "maximum": 18.953, "median": 18.641, "minimum": 14.353, "p90": 18.953 } } }, "protocol": { "prompts_sha256": "0c3ac4015813af6a1b967659bd2289990fde895a5e43ae065b894d1e1f909ecb", "prompts_version": "1.0.0", "repository_dirty": false, "repository_revision": "946394fa53db83a6865f6e3ae56594e1c299fb2a", "repository_source": "git-worktree", "repository_source_sha256": "f5503bd3fe66d96d42992fedb6cf0b47f1e7ba560359d455a6e8fc1c471261d7", "version": "1.3.0" }, "run": { "appliance": { "competing_traffic": "none intended: owner-gateway quiet window (only ALBERT+MINIME keys); client: FrankenNAS over 1 GbE LAN to the lane's rank-0 port (same path for every lane); Ling ran reasoning OFF (only way to get anything done: reasoning-on replies hung the serve) without the concurrency phase (two requests at once hang this serve) and without prefill (this serve has no /v1/completions)", "context_limit": 262144, "drafter": "model's own MTP", "hardware": "2x NVIDIA DGX Spark (GB10, 128 GB unified memory each), ascent-01 + ascent-02", "kv_cache_dtype": "engine default for this lane", "model": "bertholomus/Ling-3.1-flash-1019-EXL3-3.03bpw", "model_revision": "HF repo bertholomus/Ling-3.1-flash-1019-EXL3-3.03bpw", "negotiated_link_speed": "200 Gb/s per CX7 function", "quantisation": "EXL3 3.03 bpw weights, BF16 activations", "scheduler": "lane as served in the bake-off, unchanged", "serving_engine": "TensorFold fork github.com/bertholomus/TensorFold branch ling-3.1-tp2", "serving_image": "see serving_engine", "topology": "TP2, one rank per Spark, RoCE over ConnectX-7" }, "comparison_id": "2026-10-two-sparks-four-models-v1", "finished_at": "2026-10-29T04:06:31.018963+00:00", "label": "bakeoff-ling", "model": "rank0", "platform": "Linux-7.0.0-38-generic-x86_64-with-glibc2.43", "python": "3.11.15", "rigmark_release_check": { "ahead_by": 6, "behind_by": 0, "dirty": false, "prerelease": true, "release_url": "https://github.com/alexellis/rigmark/releases/tag/v0.0.4", "revision": "946394fa53db83a6865f6e3ae56594e1c299fb2a", "status": "ahead", "tag": "v0.0.4" }, "started_at": "2026-10-29T03:58:08.571143+00:00" }, "schema": 1, "settings": { "concurrency": [], "concurrency_runs": 3, "concurrency_tokens": 256, "concurrency_workload": "code", "decode_tokens": 8192, "extra_body": { "chat_template_kwargs": { "enable_thinking": false } }, "prefill_cache_isolation": "random-salt-per-pair", "prefill_depths": [], "prefill_runs": 3, "runs": 5, "seed": 20260905, "temperature": 0.0, "top_p": 1.0 } }